Published online Jul 28, 2018. doi: 10.3748/wjg.v24.i28.3145
Peer-review started: March 22, 2018
First decision: April 24, 2018
Revised: June 13, 2018
Accepted: June 25, 2018
Article in press: June 25, 2018
Published online: July 28, 2018
To evaluate the prognostic power of different molecular data in liver cancer.
Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis.
The mRNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index (C-index) of 0.61. For the copy number variation, methylation and miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone (P < 0.05). On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself (P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models.
In all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.
© The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
Core tip: The Cancer Genome Atlas (TCGA) is funded by the National Institute of Health to describe the genomic alterations across cancer types. Several months after the publication of liver cancer TCGA, we systemically evaluated the prognostic power of different omics data of liver cancer. We found that in all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.